Compositional mechanisms for vocabulary provide the promise of near complete coverage of health content. The Computer-based Patient Record Institute, ANSI-HISB, the NCVHS and others have all proffered statements that adequate coverage of routine medical text is necessary before the promise of controlled comparable data encoding, at the point-of-care, can be realized. Our previous work demonstrated that a compositional approach to point-of-care entry of medical problems provided superior coverage when compared to a non- compositional approach (54.0% vs. 27.6%, p<0.0001). SNOMED-CT is being positioned by the College of American Pathologists as being a standard for clinical data encoding in the United States and perhaps in the English-speaking world. To date, there is little data available from independent evaluations regarding the quality of the terminology for its proposed scope and purpose. We propose the following Specific Aims: 1) to evaluate the clinical content coverage of SNOMED-CT, and to test the hypothesis that SNOMED-CT has adequate expressivity to represent the concepts required for clinical Problem List entry, 2) to determine the Usability of compositional expressions created using SNOMED-CT, and to test the hypothesis that the Problem List Manager Application is usable and will not be a confounding factor, in terms of the clinical Usability of the SNOMED-CT as a compositional clinical terminology, and 3) to evaluate the satisfaction and performance of SNOMED-CT in a Prospective Clinical Trial using a Problem List Manager Application to test the hypothesis that SNOMED-CT is clinically usable and suitable for clinical problem list entry at the point-of-care. We will write a set of interfaces to perform Automated Term Composition and Dissection and design and test a High Throughput machine to evaluate the content coverage of SNOMED-CT using a large corpus of clinical problems (2,000,000 clinical problems). This proposed effort would provide an independent evaluation of SNOMED-CT in a clinical setting. Accurate and comparable patient data, recorded at the granularity at which we practice medicine, has far reaching research implications for medical practice (e.g., implementation of guidelines), education of future clinicians (e.g., information acquisition techniques) and research (e.g., outcomes research) toward the evidence-based practice of medicine.